Research on defect detection of improved target detection algorithm on the image surface of 5G communication ring

Abstract

In the production process of the communication circulator, defects such as solder joints, missing pieces, and wool may appear due to the current processing technology. The current communication circulator detection relies on manual experience for identification and classification. This method seriously affects production efficiency; and visual fatigue, physical condition, and working environment will affect the accuracy of manual detection; therefore, the production line of communication circulators urgently needs to be online automatic detection system. This paper will design a set of the stable automatic detection system. The main contributions are: (1) In the sample collection stage, by enhancing the limited samples, it proposes the morphological transformation of the limited defects themselves to realize the diversification of the number of samples and the morphology; (2) By reusing a sample, a cascaded Faster-RCNN and You Only Look Once (YOLO) target detection method is proposed, and the Faster-RCNN and YOLO models are trained, respectively; the model training stage uses migration learning and optimization of hyperparameters to obtain higher accuracy high training model; (3) In the inference stage, by cascading the two excellent target detection algorithms, Faster-RCNN and YOLO, this paper proposes a new decision conflict resolution algorithm, and finally obtains a higher comprehensive detection accuracy; (4) In the deployment phase, in view of the increased performance and computing power overhead caused by algorithm cascading, this paper proposes a solution for graphics processing unit shard sharing. Experimental results explain: The communication circulator surface defect detection system based on the deep learning method can effectively locate and identify various defects. The final recognition accuracy rate is about 98.5%, and the recognition speed can reach about 0.45, which is compared with traditional detection methods, which greatly improves the accuracy and speed of recognition.